〔Abstract〕 To investigate hepatocellular carcinoma ( HCC ) risk factors in hepatitis B e antigen (HBeAg)-negative cirrhotics , and to develop and validate a predictive model using these indicators . Methods A total of 649 hospitalized patients with HBeAg-negative hepatitis B cirrhosis and HBeAg-negative primary HCC were enrolled . Patients were randomly divided into a modeling group ( n = 298) and a validation group ( n = 351) at a 7:3 ratio . Logistic regression analysis was used to screen for independent predictors of HCC occurrence . A predic- tive model was constructed and validated using receiver operating characteristic ( ROC) curves . The clinical net benefit of the prediction model was assessed via decision curve analysis . Results Univariate analysis showed sig- nificant statistical differences between the modeling and validation groups in serum alanine aminotransferase (ALT) , aspartate aminotransferase ( AST) , triglycerides ( TG) , gamma-glutamyl transferase ( GGT) , red blood cell count (RBC) , hemoglobin (Hb) , platelet count (PLT) , international normalized ratio (INR) , alpha-feto- protein (AFP) , serum calcium (Ca2 + ) , serum cholinesterase (CHE) , and HBV DNA levels . Multivariate logistic regression analysis identified AST , GGT , Hb , PLT , Ca2 + , CHE , and HBV DNA as independent influencing fac- tors for HCC occurrence (P < 0. 05) , with ORs (95% CI) of 1 . 002 ( 1 . 000 - 1 . 005) , 1 . 006 ( 1 . 003 - 1 . 008) , 0. 994 (0. 988 - 0. 999) , 0. 984 (0. 981 - 0. 988) , 9. 624 (3 . 821 - 24. 245 ) , 0. 999 (0. 802 - 0. 998) , and 7. 530 (4. 143 - 13 . 687) , respectively . A nomogram prediction model was established based on these seven indi- cators . The area under the ROC curve (AUC) was 0. 936 in the modeling group and 0. 941 in the validation group . Calibration curves demonstrated high predictive accuracy of the nomogram . Conclusion AST , GGT , Hb , PLT , Ca2 + , CHE , and HBV DNA are independent risk factors for HCC development in patients with HBeAg-negative hepatitis B-related cirrhosis . The established non-invasive prediction model exhibits good discriminative ability and clinical utility , providing an experimental basis for early detection and preventive screening of HCC in this patient population .